Search Results for author: Yiheng Liu

Found 13 papers, 6 papers with code

PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models

no code implementations11 Sep 2023 Li Chen, Mengyi Zhao, Yiheng Liu, Mingxu Ding, Yangyang Song, Shizun Wang, Xu Wang, Hao Yang, Jing Liu, Kang Du, Min Zheng

Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts.

Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking

1 code implementation ICCV 2023 Yiheng Liu, Junta Wu, Yi Fu

Multi-object tracking (MOT) at low frame rates can reduce computational, storage and power overhead to better meet the constraints of edge devices.

Blocking Multi-Object Tracking

Review of Large Vision Models and Visual Prompt Engineering

no code implementations3 Jul 2023 Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, Enze Shi, Yi Pan, Tuo Zhang, Dajiang Zhu, Xiang Li, Xi Jiang, Bao Ge, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang

This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.

Prompt Engineering

Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

no code implementations4 Apr 2023 Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Lin Zhao, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, Bao Ge

This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.

Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks

1 code implementation4 Nov 2022 Yiheng Liu, Enjie Ge, Ning Qiang, Tianming Liu, Bao Ge

To validate the performance of the proposed method, we evaluate the approach on HCP-rest dataset.

Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention

1 code implementation19 May 2022 Yiheng Liu, Enjie Ge, Mengshen He, Zhengliang Liu, Shijie Zhao, Xintao Hu, Dajiang Zhu, Tianming Liu, Bao Ge

More importantly, our proposed hybrid attention modules (SA and CA) do not enforce assumptions of linearity and independence as previous methods, and thus provide a novel approach to better understanding dynamic functional brain networks.

Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling

1 code implementation29 Oct 2021 Yiheng Liu, Wengang Zhou, Qiaokang Xie, Houqiang Li

To this end, we propose to explore unsupervised person re-identification with both visual data and wireless positioning trajectories under weak scene labeling, in which we only need to know the locations of the cameras.

Scene Labeling Unsupervised Person Re-Identification

Learning Linear Non-Gaussian Graphical Models with Multidirected Edges

no code implementations11 Oct 2020 Yiheng Liu, Elina Robeva, Huanqing Wang

In this paper we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model given observational data.

An End-to-End Foreground-Aware Network for Person Re-Identification

no code implementations25 Oct 2019 Yiheng Liu, Wengang Zhou, Jianzhuang Liu, Guo-Jun Qi, Qi Tian, Houqiang Li

By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to the backgrounds, which greatly reduces the negative impacts of changing backgrounds on matching an identical across different camera views.

Person Re-Identification

Spatial and Temporal Mutual Promotion for Video-based Person Re-identification

1 code implementation26 Dec 2018 Yiheng Liu, Zhenxun Yuan, Wengang Zhou, Houqiang Li

How to explore the abundant spatial-temporal information in video sequences is the key to solve this problem.

Video-Based Person Re-Identification

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